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Enabling superior business decisions

Actions for DW Success

 

 

 

Actions for Data Warehouse Success

The following are some suggestions for building a data warehouse successfully. These are points that we deem to be of critical importance in any data warehouse initiative.

 

Establish that maintaining data quality will be an ONGOING joint responsibility between the business and IT

Organizations undertaking warehousing efforts continually discover data problems. It is best to establish up front that this project is going to entail some additional ongoing responsibility for data and data quality.

 

Train the users of the data warehouse one step at a time

Typically users are trained once. In several days they learn both the basics and intermediate and sometimes advanced aspects of using a tool. Slow down!

Consider providing training initially in the minimum needed for the user to get something useful from the tool. Then let the user use the tool for a while (meaning several days, weeks, or months). Having basic training and some hands on experience, the user will have a much better context in which to grasp the next level. Also, once the basics and the next level are learned, keep training the users! After a year using the tool, schedule advanced training.

 

Train the users about the data stored in the data warehouse

Users often need more training about the stored data than about the tools used to access the data. Do not assume the data is self-explanatory or that any metadata you may provide will answer any questions. Note that users are often used to seeing data in canned reports and as a result, seeing data in its "raw" form can be confusing.

 

Do a high level corporate data model / data warehouse architecture "exercise" upfront.

The corporate model is going to identify, at a high level, subjects and relationships and most importantly, what chunks of information are required to deliver superior business decisions. The architectural design part of the exercise intends to determine the dimensions, definitions of derived data, attribute names, and information sources that you will use in your data warehousing efforts. The exercise also determines how to keep the corporate model will be maintained and how to make sure future data warehousing efforts adhere to the architectural principles.

 

Once you know what raw data you want to feed into the data, select an appropriate tool

If you have done some research on data warehouse development, you have read that the process of extracting, transforming, and loading (ETL) takes the majority of the time in initial data warehouse development (up to 70% of the effort). In project management terms therefore, the ETL/data cleansing task is on the critical path. Furthermore, if you know what raw data you need, you are able to produce accurate business information that reflects "a single version of the truth".

 

When in a bind, ask advice of those who have done it

Carleton has designed and delivered datawarehousing solutions across Australia and New Zealand.

 

Market and sell your data warehouses

For many users, use of data warehousing systems may be optional. This means you have to identify the potential users of the systems, help them understand what are the benefits of the system, and then make them want to keep coming back to reap the benefits.

 

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